Time series forecasting using fuzzy transformation and neural network with back propagation learning.

In this study, a hybrid time series approach has been proposed in which the fuzzy transform (F-transform) is employed for its ability of handling uncertainty due to noise and multilayer perceptron with back propagation learning for its good adapting capability. F-transform is used to decompose the time series data and then those decomposed data are used as inputs and original data are used as targets in the neural networks with back propagation (BPNN) learning to capture the pattern of original time series. Proposed approach is used in Composite Index of Shanghai Stock Exchange data collected for the period January, 1993 to December, 2009. The result is compared with the result obtained by using de-noising capability of wavelet transform along with back propagation neural network. This study also includes an empirical analysis for the forecasting of Bombay Stock Exchange SENSEX data collected for the period January, 2002 to August, 2014 and Australian electricity market - Price.